An ultra-low-power multi-source heterogeneous data acquisition and transmission method and related device

Abstract

The application provides a kind of ultra-low power consumption multi-source heterogeneous data acquisition transmission method and related device, comprising: real-time pre-processing is carried out to the multi-source heterogeneous environment data of synchronous acquisition heat supply pipe network field, and effective data of light weight is obtained;According to the current communication link quality state and local cache state, the dual-mode transmission parameters of the communication module are adaptively adjusted, and the effective data is uploaded to the external receiving device through the communication module;Wherein, the dual-mode transmission at least includes long-distance low-power wide area network communication mode and cellular mobile communication mode, and the adjustment of the transmission parameters is based on the joint optimization of transmission power consumption, transmission reliability and transmission rate.The application realizes the cooperation of ultra-low power consumption operation and high reliability data transmission under the condition of energy collection power supply by the adaptive joint optimization of edge end data light weight processing and dual-mode transmission parameters, effectively improves the monitoring endurance and data continuity of heat supply pipe network under long-distance and complex environment.

Description

Technical Field

[0001] This invention belongs to the field of heating, specifically relating to an ultra-low power multi-source heterogeneous data acquisition and transmission method and related devices. Background Technology

[0002] With the acceleration of urbanization, long-distance heating pipelines are continuously extending, covering an increasingly wide area. Some pipelines even traverse remote areas such as mountains and suburbs, posing a serious challenge to traditional monitoring equipment that relies on wired power supply or periodic battery replacements. Existing monitoring technologies have significant limitations: First, in terms of power supply, wired power supply solutions are costly to install and difficult to maintain in complex geographical environments, while ordinary battery-powered equipment has limited battery life and cannot meet the needs of long-term, maintenance-free monitoring, resulting in a surge in operation and maintenance costs and the risk of monitoring interruption.

[0003] Secondly, at the data acquisition level, traditional devices mostly focus on monitoring single parameters (such as temperature or pressure), and cannot simultaneously acquire multi-dimensional heterogeneous data such as pipeline operation status, surrounding environmental weather (such as wind speed, temperature and humidity) and equipment operating conditions (such as vibration), resulting in an insufficient assessment of the pipeline health status and difficulty in accurately predicting complex faults.

[0004] In terms of data transmission and processing, existing technologies also face bottlenecks, namely: Many devices use high-power general-purpose wireless communication modules, which suffer from poor signal stability and high data packet loss rates during long-distance transmission, making it difficult to guarantee the continuity and reliability of monitoring data. Meanwhile, massive amounts of raw monitoring data are often uploaded directly to the cloud center without processing. This not only consumes valuable network bandwidth but also puts enormous pressure on cloud servers for data processing and storage, leading to system response delays and failing to meet the requirements for real-time early warning and rapid decision-making. Summary of the Invention

[0005] The purpose of this invention is to provide an ultra-low power multi-source heterogeneous data acquisition and transmission method and related device, which solves the above-mentioned shortcomings in the prior art.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows: In a first aspect, the present invention provides an ultra-low power multi-source heterogeneous data acquisition and transmission method, comprising the following steps: Real-time preprocessing of multi-source heterogeneous environmental data collected synchronously from the heating network site yields lightweight and effective data; Based on the current communication link quality status and local cache status, the dual-mode transmission parameters of the communication module are adaptively adjusted, and the effective data is uploaded to the external receiving device through the communication module. The dual-mode transmission includes at least a long-distance low-power wide-area network communication mode and a cellular mobile communication mode, and the adjustment of the transmission parameters is based on the joint optimization of transmission power consumption, transmission reliability and transmission rate.

[0007] Preferably, the multi-source heterogeneous environmental data includes at least: the medium temperature and pressure in the heating pipeline, the ambient temperature and humidity in the well chamber, the concentration of harmful gases and the depth of liquid accumulation, and the equipment vibration signal.

[0008] Preferably, the communication link quality status includes at least the received signal strength indication, signal-to-noise ratio, and bit error rate estimate; the local buffer status is the current occupancy rate of the device's built-in local data buffer.

[0009] Preferably, the adaptive adjustment of the dual-mode transmission parameters of the communication module specifically includes: Real-time monitoring of dual-mode link quality status and local cache utilization; Using the sum of weighted power consumption, transmission time, and transmission failure risk as the objective function, and with minimum transmission distance, maximum average power consumption, and buffer usage threshold as constraints, a lightweight optimization decision algorithm is run to determine the current optimal combination of dual-mode transmission parameters. Based on the optimization results, the transmission parameters of the long-distance low-power wide area network communication mode and the cellular mobile communication mode are adjusted synchronously.

[0010] Preferably, the expression for the objective function is:

[0011] in, This refers to the transmission power. For airborne transmission time; This represents the expected transmission success rate. All are scene weight coefficients.

[0012] Secondly, the present invention provides an ultra-low power multi-source heterogeneous data acquisition and transmission system, comprising: The data processing unit is used to perform real-time preprocessing of multi-source heterogeneous environmental data collected synchronously from the heating network site to obtain lightweight and effective data. The parameter adjustment unit is used to adaptively adjust the dual-mode transmission parameters of the communication module according to the current communication link quality status and local cache status, and upload the effective data to the external receiving device through the communication module. The dual-mode transmission includes at least a long-distance low-power wide-area network communication mode and a cellular mobile communication mode, and the adjustment of the transmission parameters is based on the joint optimization of transmission power consumption, transmission reliability and transmission rate.

[0013] Thirdly, the present invention provides an electronic device including a processor and a memory, wherein the memory stores computer instructions, and when the computer instructions are executed by the processor, the electronic device performs the method described thereon.

[0014] Fourthly, the present invention provides a computing device cluster, comprising at least one computing device, each computing device including a processor and a memory; The processor of the at least one computing device is configured to execute instructions stored in the memory of the at least one computing device to cause the cluster of computing devices to perform the method according to the method.

[0015] Fifthly, the present invention provides a computer program product, the computer program product including computer-executable instructions, which, when executed, implement the method described.

[0016] In a sixth aspect, the present invention provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the method described herein.

[0017] Compared with the prior art, the beneficial effects of the present invention are: This invention provides an ultra-low power multi-source heterogeneous data acquisition and transmission method. Real-time preprocessing at the edge significantly reduces redundancy and noise in the raw data, uploading only lightweight, effective features or alarm information. This not only reduces the load and transmission power consumption of the communication module from the source but also greatly alleviates the bandwidth and storage pressure on the cloud network, effectively shortening system response latency and creating conditions for real-time early warning and rapid decision-making. The dual-mode transmission architecture integrates the long-distance, low-power advantages of low-power wide-area networks with the high-speed, high-reliability advantages of cellular mobile networks, enabling the system to flexibly switch or work collaboratively in different coverage scenarios, avoiding connection blind spots of a single communication standard in remote or complex environments. More importantly, the method is not a simple combination of dual modes. Instead, it dynamically senses link quality (such as signal strength and signal-to-noise ratio) and local buffer occupancy, and jointly optimizes the three mutually constraining objectives of transmission power consumption, reliability, and speed. When the signal is good, it actively selects a high-speed, low-power parameter combination to reduce energy consumption and latency. When the signal deteriorates or the buffer approaches saturation, it automatically enhances redundancy and robustness to ensure that data is not lost. This closed-loop adaptive mechanism enables the device to maintain ultra-low power consumption to extend its battery life under the harsh conditions of energy harvesting and power supply, while ensuring data continuity and transmission success rate in long-distance and complex environments. Ultimately, it provides a comprehensive solution that combines economy, reliability, and intelligence for the health monitoring of the entire life cycle of heating pipe networks without the need for frequent manual intervention. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the method flow according to an embodiment of the present invention. Detailed Implementation

[0019] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0020] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0021] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0022] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0023] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0024] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0025] Example 1 This embodiment provides an ultra-low power multi-source heterogeneous data acquisition and transmission system, including a main control and energy harvesting module, a multi-source heterogeneous data acquisition module, a communication module, and an edge computing unit, wherein: The main control and energy harvesting module is used to provide power to the multi-source heterogeneous data acquisition module, communication module and edge computing unit.

[0026] The multi-source heterogeneous data acquisition module is used to collect environmental information, including the medium temperature and pressure in the heating pipeline, the ambient temperature and humidity in the well chamber, the gas concentration and liquid depth, and the equipment vibration signal. The edge computing unit is used to clean and compress the collected environmental information in real time to obtain processed data; extract key features from the processed data to obtain time-frequency domain features, and upload the time-frequency domain features to an external device through a communication module.

[0027] Example 2 Based on Embodiment 1, this embodiment provides an ultra-low power multi-source heterogeneous data acquisition and transmission system. The main control and energy harvesting module includes an ultra-low power main control unit and an energy harvesting unit, wherein: The ultra-low power main control unit adopts a dedicated ultra-low power microprocessor based on advanced subthreshold circuit design. Its standby current in sleep mode can be as low as microamps, which significantly reduces basic energy consumption.

[0028] The energy harvesting unit integrates a solar photovoltaic panel, a piezoelectric vibration energy harvester, and a Seebeck-based thermoelectric generator. It can capture weak energy from various environmental sources such as light, medium, and ambient temperature difference, and convert, regulate, and store it through a high-efficiency power management chip (PMIC) to provide continuous power to the system. This is the key to achieving "near-zero power consumption" operation.

[0029] The multi-source heterogeneous data acquisition module integrates various miniaturized, low-power sensors. These include a high-precision sensor for monitoring the temperature and pressure of the medium inside the pipeline, a gas sensor for detecting the temperature, humidity, oxygen, and concentration of harmful gases in the well chamber environment, a submersible level gauge for measuring water level depth, and an accelerometer for sensing equipment vibration. All sensors are connected to the main control unit via standard interfaces (such as RS485, 4-20mA) to achieve synchronous data acquisition and standardized access.

[0030] The communication module supports dual-mode transmission of LoRaWAN and 4G LTE. Its core innovation lies in its adaptive modulation and coding technology, which monitors link quality (such as signal-to-noise ratio and signal strength) in real time and dynamically selects the most suitable spreading factor, coding rate, and transmit power. When the signal is excellent, it employs a high-efficiency modulation method to increase the data rate; when the signal is weak or the distance is long, it automatically switches to a modulation method with stronger anti-interference capabilities to ensure connection reliability. This ensures ultra-long-distance coverage (several kilometers in urban environments and tens of kilometers in open areas) while minimizing communication power consumption. The module has a built-in data cache of at least 72 hours to ensure no data loss during temporary network interruptions.

[0031] The communication module in this embodiment is equipped with dual-mode transmission, which includes at least a long-distance low-power wide-area network communication mode and a cellular mobile communication mode, specifically as follows: Cross-protocol parameter collaborative optimization: Within the same hardware module, the dual-mode link quality (signal-to-noise ratio, signal strength) is sensed in real time, and the LoRa spreading factor (SF), LoRa coding rate (CR), 4G coding modulation order (MCS), and transmit power are dynamically adjusted synchronously, forming a "sense-decision-execution" closed loop across the protocol stack. Existing systems only adjust independently within a single mode, without a dual-mode joint optimization scheme.

[0032] Energy consumption-reliability joint optimization mechanism: When the signal is good, the SF of LoRa is reduced first and the MCS of 4G is increased to improve the speed; when the signal is degraded, the SF and transmit power of LoRa are increased simultaneously, and 4G is switched to a lower MCS to avoid frequent retransmissions and invalid connections, so as to achieve optimal energy consumption and reliability.

[0033] 72-hour local caching and dual-mode adaptive deep integration: The ultra-long-term network outage caching (≥72 hours) is used as the input constraint for adaptive decision-making. When the cache is full, the high reliability mode is automatically triggered (such as switching to LoRa SF12+ low-speed 4G), rather than just being used as a passive storage unit, forming an intelligent closed loop of "cache state driving communication strategy".

[0034] The edge computing unit, integrated within the main control chip, possesses local computing capabilities. It is responsible for running lightweight data preprocessing algorithms to perform real-time cleaning (e.g., outlier removal, smoothing filtering), compression, and key feature extraction (e.g., mean, peak value, and trend) on the raw collected data. Only the condensed, valid information or alarm events are uploaded, significantly reducing data uplink traffic.

[0035] Example 3 This embodiment provides an ultra-low power multi-source heterogeneous data acquisition and transmission method, including the following steps: Real-time preprocessing of multi-source heterogeneous environmental data collected synchronously from the heating network site yields lightweight and effective data; Based on the current communication link quality status and local cache status, the dual-mode transmission parameters of the communication module are adaptively adjusted, and the effective data is uploaded to the external receiving device through the communication module. The dual-mode transmission includes at least a long-distance low-power wide-area network communication mode and a cellular mobile communication mode, and the adjustment of the transmission parameters is based on the joint optimization of transmission power consumption, transmission reliability and transmission rate.

[0036] Example 4 Based on Example 3, this example provides an ultra-low power multi-source heterogeneous data acquisition and transmission method. The multi-source heterogeneous environmental data includes at least: medium temperature and medium pressure in the heating pipeline, ambient temperature and humidity in the well chamber, concentration of harmful gases and depth of liquid accumulation, and equipment vibration signal.

[0037] The communication link quality status includes at least the received signal strength indication, signal-to-noise ratio, and bit error rate estimate; the local buffer status is the current occupancy rate of the device's built-in local data buffer.

[0038] The adaptive adjustment of the dual-mode transmission parameters of the communication module specifically includes: Real-time monitoring of dual-mode link quality status and local cache utilization; Using the sum of weighted power consumption, transmission time, and transmission failure risk as the objective function, and with minimum transmission distance, maximum average power consumption, and buffer usage threshold as constraints, a lightweight optimization decision algorithm is run to determine the current optimal combination of dual-mode transmission parameters. Based on the optimization results, the transmission parameters of the long-distance low-power wide area network communication mode and the cellular mobile communication mode are adjusted synchronously.

[0039] In this embodiment, real-time sensing of the dual-mode link quality status includes: Received Signal Strength Indication (RSSI): Reflects the degree of signal attenuation and is directly related to transmission distance; Signal-to-noise ratio (SNR): measures signal quality and determines the number of modulation orders that can be supported; Bit error rate (BER) estimation: indirectly reflects transmission reliability; Cache utilization: The usage status of local cache for ≥72 hours is used as a high-priority trigger condition.

[0040] All parameters are sampled in real time by the communication coprocessor built into the main control chip, and the sampling period can be dynamically adjusted (e.g., 1–10 seconds) to ensure low overhead and high response.

[0041] In this embodiment, the objective function is:

[0042] in, Transmit power (unit: mW); Transmission time over the air (unit: ms); Expected transmission success rate; : Scene weighting coefficient (e.g., in industrial pipelines) ).

[0043] In this embodiment, the constraints are as follows: Minimum transmission distance ≥ 2 km (urban environment); Maximum power consumption ≤ 15 mA (average); When cache usage exceeds 80%, the high robustness mode will be forcibly enabled.

[0044] In this embodiment, the parameters are dynamically adjusted. Based on the optimization results, the dual-mode communication parameters are adjusted synchronously, as shown in the table below:

[0045] In this embodiment, the typical decision-making logic is as follows: Strong signal (RSSI>-90 dBm, SNR>15 dB): LoRa: SF7 + 14 dBm; 4G: MCS28 (256QAM); Objective: Maximize speed and minimize transmission time; Weak signal (RSSI < -110 dBm, SNR < 5 dB): LoRa: SF12 + 14 dBm; 4G: MCS2 (QPSK); Objective: Ensure connectivity at the expense of speed.

[0046] Cache full (>80%) Forced switch to LoRa SF12 + 14 dBm prioritizes alarm event uploading even with good 4G signal. In this embodiment, the system closed loop and feedback correction are as follows: After each transmission, the system records the actual transmission result (ACK / NACK, number of retransmissions, energy consumption). The results are fed back to the optimization model for online incremental learning, gradually correcting the parameter mapping relationship. A global parameter calibration is performed every 24 hours to adapt to seasonal environmental changes.

[0047] Example 5 This embodiment also provides a computing device. The computing device includes a bus, a processor, a memory, and a communication interface. The processor, memory, and communication interface communicate with each other via the bus. The computing device can be a server or a terminal device. It should be understood that this application does not limit the number of processors and memory in the computing device.

[0048] A bus can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, a bus can include a path for transmitting information between various components of a computing device (e.g., memory, processor, communication interfaces).

[0049] The processor may include any one or more of the following: central processing unit (CPU), graphics processing unit (GPU), tensor processing unit (TPU), application specific integrated circuit (ASIC), field-programmable gate array (FPGA), microprocessor (MP), or digital signal processor (DSP).

[0050] Memory can include volatile memory, such as random access memory (RAM). Processors can also include non-volatile memory. volatile memory, such as read-only memory (ROM). ROM (memory only), flash memory, hard disk drive (HDD), or solid state drive (SSD).

[0051] The memory stores executable program code, which the processor executes to implement the functions of the aforementioned units, thereby achieving, for example, the method described in Embodiment 1. That is, the memory may store instructions for the methods and functions relating to the computing device in any of the above embodiments.

[0052] The communication interface uses transceiver modules such as, but not limited to, network interface cards and transceivers to enable communication between computing devices and other devices or communication networks.

[0053] Example 6 This embodiment also provides a computing device cluster. The computing device cluster includes at least one computing device. The computing device can be a server, such as a central server, an edge server, or a local server in a local data center. In some embodiments, the computing device can also be a terminal device such as a desktop computer, a laptop computer, or a smartphone.

[0054] The computing device cluster includes at least one computing device. The memory of one or more computing devices in the computing device cluster may store the same instructions for performing the methods and functions related to the computing devices in any of the above embodiments.

[0055] In some possible implementations, the memory of one or more computing devices in the computing device cluster may also store partial instructions for performing the methods and functions of the computing devices involved in any of the above embodiments. In other words, a combination of one or more computing devices can jointly execute the instructions for performing the methods and functions of the computing devices.

[0056] It should be noted that the memory in different computing devices within a computing device cluster can store different instructions, which are used to execute parts of the device's functions.

[0057] In some possible implementations, one or more computing devices in a computing device cluster can be connected via a network. This network can be a wide area network (WAN) or a local area network (LAN), etc. Two computing devices are connected via the network. Specifically, they connect to the network through communication interfaces on each computing device.

[0058] Embodiments of this disclosure also provide a computer program product containing instructions that, when run on a computer, cause the computer to perform the methods and functions related to a computing device in any of the above embodiments.

[0059] Example 7 This embodiment also provides a computer-readable storage medium storing computer instructions that, when executed by a processor, cause the processor to perform the methods and functions of the computing device involved in any of the above embodiments.

[0060] Generally, the various embodiments of this disclosure can be implemented in hardware or dedicated circuitry, software, logic, or any combination thereof. Some aspects can be implemented in hardware, while others can be implemented in firmware or software, which can be executed by a controller, microprocessor, or other computing device. Although various aspects of the embodiments of this disclosure are shown and described as block diagrams, flowcharts, or represented using some other illustration, it should be understood that the blocks, apparatuses, systems, techniques, or methods described herein can be implemented as, as non-limiting examples, in hardware, software, firmware, dedicated circuitry or logic, general-purpose hardware or controllers or other computing devices, or some combination thereof.

[0061] Example 8 This embodiment provides at least one computer program product tangibly stored on a non-transitory computer-readable storage medium. The computer program product includes computer-executable instructions, such as instructions included in program modules, which execute in a device on a target real or virtual processor to perform the processes / methods as described above with reference to the accompanying drawings. Typically, program modules include routines, programs, libraries, objects, classes, components, data structures, etc., that perform specific tasks or implement specific abstract data types. In various embodiments, the functionality of program modules can be combined or divided among program modules as needed. The machine-executable instructions for the program modules can execute within a local or distributed device. In a distributed device, the program modules can reside in both local and remote storage media.

[0062] Computer program code used to implement the methods of this disclosure may be written in one or more programming languages. This computer program code may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus, such that when executed by the computer or other programmable data processing apparatus, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be performed. The program code may be executed entirely on a computer, partially on a computer, as a stand-alone software package, partially on a computer and partially on a remote computer, or entirely on a remote computer or server.

[0063] In the context of this disclosure, computer program code or related data may be carried on any suitable carrier to enable a device, apparatus, or processor to perform the various processes and operations described above. Examples of carriers include signals, computer-readable media, and so on. Examples of signals may include electrical, optical, radio, sound, or other forms of propagation signals, such as carrier waves, infrared signals, etc.

[0064] Computer-readable media can be any tangible medium that contains or stores programs for or relating to an instruction execution system, apparatus, or device, or a data storage device such as a data center containing one or more available media. Computer-readable media can be computer-readable signal media or computer-readable storage media. Computer-readable media can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. More detailed examples of computer-readable storage media include electrical connections with one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0065] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for ultra-low power multi-source heterogeneous data acquisition and transmission, characterized in that, Includes the following steps: Real-time preprocessing of multi-source heterogeneous environmental data collected synchronously from the heating network site yields lightweight and effective data; Based on the current communication link quality status and local cache status, the dual-mode transmission parameters of the communication module are adaptively adjusted, and the effective data is uploaded to the external receiving device through the communication module. The dual-mode transmission includes at least a long-distance low-power wide-area network communication mode and a cellular mobile communication mode, and the adjustment of the transmission parameters is based on the joint optimization of transmission power consumption, transmission reliability and transmission rate.

2. The ultra-low power multi-source heterogeneous data acquisition and transmission method according to claim 1, characterized in that, The multi-source heterogeneous environmental data includes at least: the temperature and pressure of the medium in the heating pipeline, the ambient temperature and humidity in the well chamber, the concentration of harmful gases and the depth of liquid accumulation, and the equipment vibration signal.

3. The ultra-low power multi-source heterogeneous data acquisition and transmission method according to claim 1, characterized in that, The communication link quality status includes at least the received signal strength indication, signal-to-noise ratio, and bit error rate estimate; the local buffer status is the current occupancy rate of the device's built-in local data buffer.

4. The ultra-low power multi-source heterogeneous data acquisition and transmission method according to claim 1, characterized in that, The adaptive adjustment of the dual-mode transmission parameters of the communication module specifically includes: Real-time monitoring of dual-mode link quality status and local cache utilization; Using the sum of weighted power consumption, transmission time, and transmission failure risk as the objective function, and with minimum transmission distance, maximum average power consumption, and buffer usage threshold as constraints, a lightweight optimization decision algorithm is run to determine the current optimal combination of dual-mode transmission parameters. Based on the optimization results, the transmission parameters of the long-distance low-power wide area network communication mode and the cellular mobile communication mode are adjusted synchronously.

5. The ultra-low power multi-source heterogeneous data acquisition and transmission method according to claim 4, characterized in that, The expression for the objective function is: in, This refers to the transmission power. For airborne transmission time; This represents the expected transmission success rate. All of these are scene weight coefficients.

6. A low-power multi-source heterogeneous data acquisition and transmission system, characterized in that, include: The data processing unit is used to perform real-time preprocessing of multi-source heterogeneous environmental data collected synchronously from the heating network site to obtain lightweight and effective data. The parameter adjustment unit is used to adaptively adjust the dual-mode transmission parameters of the communication module according to the current communication link quality status and local cache status, and upload the effective data to the external receiving device through the communication module. The dual-mode transmission includes at least a long-distance low-power wide-area network communication mode and a cellular mobile communication mode, and the adjustment of the transmission parameters is based on the joint optimization of transmission power consumption, transmission reliability and transmission rate.

7. An electronic device, characterized in that, It includes a processor and a memory, the memory storing computer instructions that, when executed by the processor, cause the electronic device to perform the method of any one of claims 1 to 5.

8. A computing device cluster, characterized in that, It includes at least one computing device, each computing device including a processor and memory; The processor of the at least one computing device is configured to execute instructions stored in the memory of the at least one computing device to cause the cluster of computing devices to perform the method according to any one of claims 1 to 5.

9. A computer program product, characterized in that, The computer program product includes computer-executable instructions that, when executed, implement the method of any one of claims 1 to 5.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions that, when executed by a processor, implement the method of any one of claims 1 to 5.

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